CALIFORNIA STATE UNIVERSITY, NORTHRIDGE. Online Social Support: Just as Valuable as Offline Social Support?

Transcription

1 CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Online Social Support: Just as Valuable as Offline Social Support? A thesis submitted in partial fulfillment of the requirements For the degree of Master of Arts in Psychology, Clinical Psychology By Ian Merion August 2017

2 The thesis of Ian Merion is approved: Dr. Mark Otten Date Dr. Sara Berzenski Date Dr. Gary Katz, Chair Date California State University, Northridge i

3 Table of Contents Signature Page Abstract Introduction Literature Review Social Support Received vs. Perceived Social Support Social Support and Mental/Physical Health Evidence Against the Utility of Online Support Evidence Supporting the Utility of Online Support Mixed Findings for the Utility of Online Support Psychological/Subjective Well-being Significance Aims and Hypotheses Methods Participants Measures Procedure Results Discussion References i iii ii

4 Abstract Online Social Support: Just as Valuable as Offline Social Support? By Ian Merion Master of Arts in Psychology, Clinical Psychology Over the past 15 years, internet and phone technologies have introduced new ways to communicate. Substantial research has shown that social support can positively impact psychological well-being. However, few studies have explored the varying impacts of online and offline social support. This study measures how offline and online social support differentially influence psychological well-being. To do this, the Multidimensional Scale of Perceived Social Support (MSPSS) was adapted for both offline and online contexts. To validate these altered scales, the Social Support Questionnaire was administered to see how strongly the measures correlate. To measure wellbeing, the Ryff Scales of Psychological Well-being was administered as well. Finally, the Marlowe- Crowne Social Desirability scale was administered to see if participants presented themselves in an overly positive light. Results confirmed the reliability and validity of both offline and online versions of the MSPSS. Both offline and online social support predicted increased well-being. There were no iii

5 significant differences in online social support between online game players and those who spent little time playing online games. Those who spent less time playing online games with friends did not have any meaningful difference in offline support compared to those who spent more time playing games. Finally, online support was a slightly better predictor of well-being than offline support. iv

6 Introduction Online Social Support: Just as Valuable as Offline Social Support? The pervasive "lonely gamer" stereotype depicts gamers as young, addicted males with few real life social ties, but a recent study found a very different picture. Schiano et al (2013) found that World of Warcraft (WoW) players increased their real life social ties by meeting people online that turned into real life friends. While many will agree that real life social ties are beneficial, few have looked at the impact of online social ties and the support they can provide. The addition of Online Gaming Disorder in section 3 of the DSM-5 reminds us that gaming in excess can be harmful (American Psychiatric Association, 2013). Concern for the well-being of players is compounded by the fact that the average WoW player spends a part time job's worth of time online per week. In one study using questionnaires, WoW players reported playing for an average of 25 hours per week (Longman et al. 2009). Similar numbers have been found in other studies (Utz et al. 2012; Cole & Griffiths, 2007). While many have pointed out the potentially negative consequences of problematic internet use, few have measured the effects of online perceived social support that gamers and others receive. With the internet in the hands of so many people, research into online social support could be beneficial to society at large. 1

7 Literature Review Social Support Social support can be defined as the exchange of resources between two or more people, where the provider or recipient perceives this support to be intended to enhance the well-being of the recipient (Schumaker & Brownell, 1984). Social support comes in four main types types: emotional, tangible, informational and belonging (Glanz, 2015; Schaefer, 1981; Langford, 1997; Taylor, 2010). Emotional social support refers to when a person offers empathy, affection, or concern to another person (Glanz, 2015; Langford, 1997; Schaefer, 1981; Taylor, 2010). This type of support can be exchanged in an online environment through text and voice chat. Tangible/Instrumental social support involves direct aid or services, such as borrowing money (Glanz, 2015; Schaefer, 1981; Taylor, 2010; Langford, 1997). The exchange of this type of support is possible through the exchange of virtual items/currency, as well as real money through electronic transactions. Informational social support involves giving information and/or advice to another person (Schaefer, 1981; Taylor, 2010; Langford, 1997; Glanz, 2015). For example, informational support can occur when an individual helps another to understand a stressful event better (Taylor, 2010). Players can give advice on everything from in-game objectives to real life problems. Finally, belonging support can be described as having others to engage with in social activities (Glanz, 2015). Online games can offer belonging support as players team up and form groups known as "guilds" or "clans". These groups can then facilitate the exchange of emotional and informational support as guild members play together, conversing through voice and text chat. Received vs. Perceived Social Support In studying social support, the distinction between received and perceived social support is 2

8 critical. Received social support is considered an objective measure of the amount of support received, while perceived social support is a subjective evaluation of how helpful a relationship is to someone's life (Taylor, 2010; Schaefer, 1981). These concepts are only loosely correlated with each other (Glanz, 2015; Melrose, 2014). The literature suggests that whereas perceived social support is associated with positive health outcomes (Barrera, 2010; Holt-Lunstad et al. 2010; Uchino, 2004), received social support has been an inconsistent predictor of health (Bolger & Amarel, 2007; Uchino, 2009). Other studies have found mixed results, with both received and perceived social support contributing to life satisfaction (Siedlecki, 2013). Melrose (2014) found that the relationship between received support and well-being is mediated by the need for support; received support only had a positive impact on mental health when that support was needed. Because research supports the strength of perceived social support over received social support, perceived social support is chosen as a predictor variable. Social Support and Mental/Physical Health In a study of happiness and success, Lyubomirsky and colleagues (2005) found that offline social support is one of the best correlates with well-being. A meta-analysis of 148 studies showed that those with stronger social relationships were 50% more likely to survive than those with weaker social relationships (Holt-Lunstad, 2010). Several studies have identified a link between social support and life satisfaction (Siedlecki, 2013; Kong, 2011). In addition, several studies have confirmed a link between social support and self-esteem (Sari, 2014; Kong, 2011). Findings from Kapikiran (2013) suggest that self-esteem and social support are partial mediators between loneliness and satisfaction. The negative relationship between loneliness and life satisfaction became weaker when social support and self-esteem were taken into account. In a work environment, Stansfeld and colleagues (2013) found that higher well-being at work was predicted by higher levels of confiding/emotional support. 3

9 These studies suggest a potential relationship between social support and well-being, but all measure social support without asking about online friends. While many studies have established that gamers obtain social support through online games, it's not clear whether or not this online support can lead to improved psychological/physical well-being. One study found that the association between online social support and psychosocial well-being is mainly dependent on people's purposes, personalities, and contexts of using the technology. For example, using the internet to communicate with family and friends had a positive impact on wellbeing. In the same study, however, it was found that the intensity of player communication and the size of their in-game social network was associated with increased loneliness. The biggest factor, however, was extraversion, which predicted psychosocial well-being better than any other variable (Shen & Williams, 2011). Other online environments have also been shown to improve perceived social support and subsequent well-being. One study of users of Facebook found that number of Facebook friends was a strong predictor of perceived social support (Nabi et. al. 2013). In addition, a study of Facebook users found that self-disclosure had a positive, significant effect on social support and online social wellbeing (Huang, 2016). Huang (2016) also found that social support had a significant positive impact on online social well-being. While many studies seek a causal relationship between time spent playing and well-being, one study sought to find whether psychosocial well-being was a cause or a consequence of time spent playing. The results suggest that individuals play online games to compensate for pre-existing social difficulties (Kowert et al. 2015). The study disputed the claim that online game play has negative effects on psychosocial well-being. Kowert et al. (2015) also found that adolescents' well-being increased more over the course of 1 year if they played online video games. These findings suggest 4

10 that the inner mechanisms of online gaming (which may include online social support) are beneficial to mental health. Evidence Against the Utility of Online Social Support Past research has not separated online and offline perceived support, which is essential to answering this question. In a study of perceived social support and depression in players of WoW, Dupuis & Ramsey (2011) theorize that the significant relationship found between perceived social support and lower depression comes from relationships other than those from the game environment. Nonetheless, it is impossible to confirm Dupuis and Ramsey's results since they measured perceived social support across both offline and online relationships. Although this study found no impact on perceived social support from playing games, future research should separate the effects of offline and online perceived social support in order to fully answer this question. Evidence Supporting the Utility of Online Social Support There seems to be many social benefits from playing Massively Multiplayer Online Games (MMOGs). Surveys and behavioral data gathered from a group of 3,500 players of an MMOG called Everquest II indicated that social structures and communication processes in MMOGs contribute to trust development (Ratan et al. 2010). The anonymity provided by MMOGs makes self-disclosure easier, supporting the claim that these online spaces provide social support in a way that is unavailable in other societal realms (Ratan et al. 2010). Another study found that 75% of 912 MMOG players made "good friends" while playing (Cole & Griffiths, 2007). Though "friend" is never operationalized in this study, it can be inferred to mean an individual with whom one shares some sort of positive relationship. Cole & Griffiths (2007) also found that 45.6% of participants believed that their online 5

11 friends were comparable with their offline friends, and 16.8% said they weren't sure. Supporting Ratan's findings, research supports that players can express themselves more openly and self-disclose more information in online environments than offline environments. One study found that virtual gaming may allow players to express themselves in ways they may not feel comfortable doing in real life because of their appearance, gender, sexuality and/or age (Cole & Griffiths, 2007). One factor that contributes to the online disinhibition effect is dissociative anonymity (Suler, 2004). Dissociative anonymity allows the user to feel as though their online persona is someone other than themselves, thereby diffusing responsibility for one s actions (Suler, 2004). This can lead to more personal disclosure, as well as socially aggressive behavior (e.g., hurting someone s feelings with a negative remark). Computer Mediated Social Support (CMSS) creates anonymity, making it easier to overcome stigma/shame associated with seeking help (King & Moreggi, 1998). This disinhibition effect is good news for those suffering from social anxiety. Subrahmanyam & Greenfield (2008) found that, among adolescents with social anxiety, communicating with strangers online can decrease social anxiety. Though it wasn't mentioned in the study, decreased social anxiety would likely lead to increased well-being. Another important social factor is the nature of relationships between players. According to one study of WoW players, about 11% of gamers had dated other players before, and 26% of players played with offline friends and family members (Cole & Griffiths, 2007). Levels of problematic WoW play were found to be lower in players that played with 'real life' friends and relations (Snodgrass et al. 2011). Meng and colleagues (2015) brought an important concept to gaming literature: multimodal communication. They found that gamers who used more modes of communication (Skype, Facebook, voice chat, in-game chat, etc.) had increases in bridging and bonding social capital. Bridging social 6

12 capital consists of people that give information, whereas bonding social capital consists of people that lend emotional support. It may be the case, then, that those using more modes of communication will have higher perceived social support from online sources, which may increase well-being. Being helped in a game may provide the same perceived support we naturally crave in offline interactions. One study found that, in a social networking game called Cityville, being helped in the game increased the feeling of proximity (copresence) in a virtual environment. Copresence was correlated with all three types of perceived social support (Wohn, 2016). This suggests that online interactions have the ability to transfer valuable social support. There are distinct advantages to receiving social support online. For example, one is not restricted by time or location when receiving support online (Jones, 1997). In addition, the internet has a great number of participants from which support can be drawn (Chen & Choi, 2011). Those that are socially anxious may get more satisfaction from online support than offline support (Indian & Grieve, 2014). Additionally, personal disclosure is much easier in an online context than in an offline context (Cole & Griffiths, 2007; King & Moreggi, 1998). Mixed Findings for the Utility of Online Social Support There are two theories about how the internet affects the social lives of users. Displacement theory argues that time online, though often spent communicating, displaces higher quality face-to-face communication with close friends and family (Kraut et al. 2002). Conversely, the theory of augmentation suggests that using the internet can improve existing social ties. In 1998, Kraut and colleagues found that more time spent online was associated with decreased psychological well-being. However, in a follow-up study four years later, Kraut re-examined his sample and found that these negative effects dissipated in veteran users (Kraut et. al. 2002). Kraut and colleagues (2002) postulate 7

13 that the difference in findings between the two studies may be explained by maturation of the participants, differences in samples between studies 1 and 2, and changes in the internet itself. His findings also supported the "rich get richer" hypothesis, where extraverts and those with more social support had better outcomes from using the internet than introverts and those with less social support (Kraut et. al. 2002). The social compensation hypothesis suggests that individuals with low levels of social resources (greater social anxiety, poor social skills, few offline friends, etc.) are drawn to online video gaming (Kowert, 2015). This would explain why some studies show a link between online gaming and depression and loneliness (Kowert, 2015). These individuals might appreciate visual anonymity and the lack of non-verbal cues. Conversely, there's some evidence to suggest that online gaming may increase social competence by training social skills, increasing social self-esteem, and developing online friendships (Kowert, 2015). In a review of internet use and well-being, Kraut & Burke (2015) found that levels of depression vary depending on the amount of initial support received and what the internet is being used for. Although higher internet use led to increased depression in their model, this was not the case if the internet was being used to communicate with friends and family. Conversely, using the internet to meet new people was associated with greater depression. It is worth noting that the internet can be used for many non-social purposes, including retrieving and using information, seeking entertainment or escape, shopping, and acquiring health information or talking about health (Kraut & Burke, 2015). Dividing activities in this way avoids the pitfall of categorizing users solely under low, medium, and high internet users (Kraut & Burke, 2015). Those that used the internet to communicate with weak ties show increases in depression, while those that communicated with strong ties showed decreases in depression (Kraut & Burke, 2015). Interestingly, those with more initial social resources offline 8

14 showed greater increases in depression due to internet use. In addition, Kraut & Burke (2015) found that receiving one-on-one communication through social media from one's strong ties, such as private messages, wall posts, and comments, increased perceived social support, happiness and self-reported health while decreasing depression, loneliness, negative affect and stress. In addition, the effect size of communication with strong ties was comparable to the effect size of major life events, including getting married, having a new baby, or losing one's job (Kraut & Burke, 2015). Psychological/Subjective Well-being Well-being can be measured in several ways. Two perspectives dominate research on wellbeing; the hedonic perspective and the eudemonic perspectives (Casas, 2010). The hedonic perspective adopts the perspective of subjective well-being and focuses on happiness, defining well-being in terms of pleasure attainment and pain avoidance. The eudaimonic perspective adopts the notion of psychological well-being and focuses on meaning and self-realization, defining well-being in terms of the degree to which a person is fully functioning. Although the hedonic perspective is important to understanding why people do certain things to feel good, the present study will focus on eudaimonic perspective as it affords a broader view of well-being. There are several components to psychological well-being, including self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth. Significance This study is one of the few in which the main goal is to determine the power of online social support. Many studies have already found that people can fulfill many social goals online, including making and keeping friends, interacting with family, and even meeting significant others (e.g., Cole & 9

15 Griffiths, 2007). However, research has not quantified this online social support in a meaningful way. This author knows of no available social support questionnaires that quantify perceived online social support. This study also breaks ground by measuring psychological well-being alongside social support in order to examine any possible correlations between the two (and how this varies between offline and online support, which has not yet been done to this author's knowledge). Finally, this project includes another social support questionnaire to be correlated with offline and online versions in order to confirm the measure's validity. The social desirability component protects against those who would be disingenuous about their true feelings, or the true nature of their social support network. Aim 1: Validating the Measures of Online and Offline Support Hypothesis 1: The offline and online versions of the Multidimensional Scale of Perceived Social Support (MSPSS) will correlate highly with scores on the Social Support Questionnaire (SSQ). Hypothesis 2: Both versions of the MSPSS will have good reliability. Hypothesis 3: The factor models of both versions of the MSPSS will be similar. Aim 2: Evaluating Online and Offline Support Hypothesis 1: Increased online social support will predict increased well-being. Hypothesis 2: Increased offline social support will predict increased well-being. Hypothesis 3: Those that spend less time playing online games with friends will receive a greater impact on well-being from offline social support than those that spend more time playing online games with friends. Hypothesis 4: Those that spend more time playing online games with friends will receive a greater impact on well-being from online social support than those that spend less time playing online games with friends. 10

16 Hypothesis 5: Offline social support will have a greater impact on well-being than online social support. 11

17 Methods Participants Participants recruited through SONA were college students years of age. SONA is CSUN's online extra credit option where students participate in psychology studies. Undergraduate students were compensated with course credit based on every ten minutes they spend filling out this questionnaire if they are enrolled in two different introductory psychology courses. Measures Multidimensional Scale of Perceived Social Support (MSPSS) The MSPSS is a well validated and widely used measure of perceived social support from family, friends and significant others (Zimet et al. 1990). The measure uses a 6-point Likert type scale (very strongly disagree to very strongly agree). The MSPSS has three subscales: Family, Friends and Significant Others. Higher scores indicate more support while lower scores indicate less support. The MSPSS is a continuous measure. In prior research, the MSPSS was found to have strong internal consistency with α coefficients of.91 for the total score,.90 for the family subscale,.94 for the friends subscale and.95 for the significant others subscale. For total scores, test-retest reliability over a 2-3 month period was.85. The sample had a total score Cronbach alpha of.94 for the offline version and.93 for the online version. For the offline scale, the special person subscale had a Cronbach's alpha of.967, the family subscale was.948, and the friends subscale was.960. For the online scale, the friends subscale had a Cronbach alpha of.957, the special person subscale was.937, and the family subscale was.927. Two versions of the MSPSS will be presented; one specifically measuring offline support and one specifically measuring online support. The online social support version will 12

18 ask participants to only include support that they receive from phone calls, texts, and internet use. The offline social support version asked participants to include support they receive from offline contexts. Social Support Questionnaire (SSQ) To validate offline and online versions of the MSPSS, the full version of Sarason's Social Support Questionnaire (SSQ) was administered. The full version asks respondents to provide people they can count on to provide support in 27 different situations. The SSQ has good reliability and validity and is 27 questions long (Sarason et al. 1983). Inter-item correlations ranged from.35 to.71, with a mean of.54. Corrected item-total correlations were between.51 and.79. The alpha coefficient of internal reliability was.97 (McDowell & Newell, 1996). The overall support score is calculated as a mean of the number of participants listed for each question. The overall satisfaction score is calculated as a mean of the satisfaction scores. Overall support scores will range from 0 to 9 and overall satisfaction scores will range from 1 to 6. Higher scores indicate more support while lower scores indicate less support. The SSQ is a continuous measure. Ryff Scale of Psychological Well-being For psychological well-being, the full Ryff scale of psychological well-being was used. This scale was constructed to measure the dimensions of autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance (Ryff, 1989). Dimensions have 14 items each. Participants use a 6-point Likert-type scale ranging from strongly disagree to strongly agree. The coefficient alpha for each dimension is.86 for environmental mastery,.83 for autonomy,.85 for personal growth,.88 for positive relations with others,.88 for purpose in life, and.91 for self-acceptance (Ryff, 1989). This 84 item test 13

19 takes about minutes to complete. Scores on each dimension range from 1-6. This is calculated by taking the mean of the responses for all questions in a given dimension. To find a total score, one calculates the mean of all dimension scores. These scores also range from 1-6. Marlowe-Crowne Social Desirability Scale In addition, the Marlowe-Crowne (1960) Social Desirability Scale controlled for any motivation to provide socially desirable results. This measure contains 33 true or false questions and correlates highly with MMPI scales related to social desirability (Crowne & Marlowe, 1960). The internal consistency coefficient for the form is.88 (Crowne & Marlowe, 1960). Scores range from 0 to 33, with higher scores indicating more socially desirable responses. Respondents gain a point for each response that is socially desirable. The scale is discrete. Demographics Finally, a demographics measure measured the amount of time spent playing online games with a friend per week, age, gender, and relationship status. Procedure This study was posted online through SONA. Qualtrics was utilized to translate the items onto a computer. Pilot testing indicated that the entire battery would take approximately 30 minutes to complete the 168 questions online. The survey was accessible via a provided link. A brief introduction to the study will be presented above the link. Upon clicking the link, participants were asked to provide consent to participating in the study, as well as verify that they were 18 years or older. The measures were presented in the following order; MSPSS online version, MSPSS offline version, SSQ, Ryff scale of psychological well-being, Demographics and the Marlowe-Crowne Social Desirability Scale. Once the participant was complete, they were debriefed about the deception used in the social desirability 14

20 scale. 15

21 Results The sample contained 41 males and 43 females. Fifty-five participants indicated they were single, 28 said they were in a relationship, and one said they were married. Twenty-five participants were 18 years old, 27 were 19 years old, 14 were 20 years old, and the remaining participants were older with a maximum of 26. Fifty-six participants indicated they spent no time playing online games with friends. The rest ranged from 1 to 26 hours spent online gaming. Table 1 Gender Frequencies Frequency Percent Male Female Total Missing Table 2 Relationship Status Frequencies Frequency Percent Single In a Relationship Married

22 Total Missing Table 3 Age Frequencies Frequency Percent Total Missing Total

23 Table 4 Frequencies for Hours Per Week Spent Online Gaming Frequency Percent

24 Total Table 5 Descriptives of Dependent Variables Mean SD Skewness SSQ Satisfaction Score Ryff Well-being Score To measure reliability, the Cronbach's alpha was measured for offline and online MSPSS forms and their subscales. Cronbach's alpha for the online scale was.933 and.943 for the offline scale. For the offline scale, the special person subscale had a Cronbach's alpha of.967, the family subscale was.948, and the friends subscale was.960. For the online scale, the friends subscale had a Cronbach alpha of.957, the special person subscale was.937, and the family subscale was.927. These internal consistency measures were very similar to previously published reliability estimates of the MSPSS. The online and offline MSPSS total scores were similarly correlated with the SSQ satisfaction score, having correlations of.649 ( p <.001) and.664 ( p <.001), respectively. The online and offline MSPSS scores share a high correlation of.874 ( p <.001). The factor analysis of the offline and online MSPSS items shows that the two forms have similar factor structures. Both support a 3-factor solution with 3 eigenvalues above 1. For the online 19

25 MSPSS, the first factor explained 59% of the variance, the second factor explained an additional 15%, and the third factor explained an additional 11%. For the offline MSPSS, the first factor explained 63% of the variance, factor 2 explained an additional 17%, and factor 3 explained an additional 10%. The rotated factor matrix on both measures (tables 6 and 7) showed that items loaded onto a factor according to which subscale they were a part of (friends, family or significant other). As expected, the factor structure was similar for both measures even if the amount of accounted variance in each factor did differ. Scree plots for both the offline and online versions, as indicated by figures 1 and 2, showed a lot of explained variance for the first three factors and practically none for the the 4 th. The scree plots look nearly identical, showing the similarities in factor structure between the two versions. 20

26 Figure 1. Factor breakdown for online version of the MSPSS showing a 3-factor solution. Figure 2. Factor breakdown for online version of the MSPSS showing a 3-factor solution. 21

27 Table 6 Rotated Factor Matrix for items on the offline version of the MSPSS. Item Factor 1: Special person Factor 2: Friends (SE) Factor 3: Family (SE) (SE) 1 special person / in need 2 special person / share joy/sorrow 3 family / help family / emotional help 5 special person / source of comfort 6 friends / help friends / count on family / talk re: problems 9 friends / share joy/sorrow 10 special person /

28 cares feelings 11 family / help decisions 12 friends / talk re: problems Table 7 Rotated Factor Matrix for online version of the MSPSS Item Factor 1: Friends (SE) Factor 2: Special Person Factor 3: Family (SE) (SE) 1 special person / in need 2 special person / share joy/sorrow 3 family/help family / emotional help 5 special person / source of comfort 6 friends / help

29 7 friends / count on family / talk re: problems 9 friends / share joy/sorrow 10 special person / cares feelings 11 family / help decisions 12 friends / talk re: problems A linear regression was performed with online social support and social desirability predicting psychological well-being. This allows us to measure online social support's impact on psychological well-being while controlling for social desirability. Online social support significantly predicted psychological well-being, while controlling for social desirability (β =.364, p <.001). Another linear regression was done with offline social support and social desirability predicting well-being. Offline social support significantly predicted psychological well-being, while controlling for social desirability (β =.35 p <.001). Fisher's r-to-z transformations were not done because the correlations were so similar. Also, because offline and online versions were so highly correlated, this justifies not placing them in the same regression model. Hours spent playing online games did not impact the relationship between online social support 24

30 and well-being, p =.782. Similarly, hours spent playing online games did not impact the relationship between offline social support and well-being, p =.760. Table 8 Summary of Regression Analysis of Variables Predicting Psychological Well-being (N = 85) Variable B SE B β p Online social support Offline social support Online social support X Hours spent playing online games with friends Offline social support X Hours spent playing online games with friends 25

31 Discussion There were high correlations between online and offline MSPSS forms and the satisfaction scores from the SSQ, suggesting that the MSPSS forms and the SSQ were tapping into the same construct: perceived social support. These high correlations provide validity for the newly tested forms of the MSPSS and confirm hypothesis 1 of aim 1. The high Cronbach alpha on both the offline and online MSPSS suggest high reliability. High Cronbach alphas for the subscales of each measure indicate high internal consistency for each subscale. This is expected because both the online and offline MSPSS are broken down into 3 subscales. These findings confirm hypothesis 2 of aim 1. These findings also support past evidence from a population of urban adolescents, which demonstrated high internal consistency and 3 factors lining up with the subscales of the MSPSS (Canty-Mitchel & Zimet, 2000). An earlier study found good internal reliability and strong validity across 3 different subsamples (Zimet et al. 1990). To summarize, all three hypotheses of aim 1 were supported. Online and offline MSPSS scores correlated highly with scores on the SSQ, supporting hypothesis 1. Online and offline versions of the MSPSS had high internal consistency, confirming hypothesis 2. Finally, online and offline versions of the MSPSS had similar factor structures, confirming hypothesis 3. A significant positive correlation was found for online social support predicting well-being, confirming hypothesis 1 of aim 2. This correlation is expected, as past research shows many ways through which phones and the internet can increase well-being through social support. One study of social networking sites found that perceived social support was positively associated with life satisfaction (Oh, Ozkaya, & Larose, 2014). Life satisfaction is one component of psychological wellbeing, so an increase in life satisfaction would likely indicate an increase in psychological well-being. Another study found that internet communication decreased depression (Shaw & Gant, 2002). A decrease in depression would likely increase psychological well-being, as sense of well-being would 26

32 definitely be affected by depressive thoughts. A significant positive correlation was found for offline social support predicting well-being, confirming hypothesis 2 of aim 2. This finding was expected due to the literature suggesting a link between social support and well-being. According to Ahn (2016), we should expect a moderate correlation between social support and psychological well-being. As expected, correlation sizes fall into the medium range. The size and significance of the correlations were similar due to the very high correlation between online and offline versions of the MSPSS. This additional finding suggests that offline and online support similarly predicted well-being. If someone could be relied upon in an offline setting, it's likely they were also available in an online setting as well, and vice versa. This suggests that online and offline friends did not fall into different groups; instead, offline and online groups likely consisted of the same people. The correlation between online social support and well-being the same as the correlation between offline social support and well-being, disconfirming hypothesis 5. We expected offline support to be more valuable than online support in terms of indicating well-being, but the data did not support this conclusion. Offline support undoubtedly includes features that online support doesn't, such as physical proximity and interaction. However, online support has anonymity, which frees inhibitions. Few studies have compared the correlations of offline vs. online support and well-being; more research is needed in this area. The non-significant impact of hours spent playing online games on the relationship between online social support and well-being disconfirms hypothesis 3. Additionally, the non-significant impact of hours spent playing online games on the relationship between offline social support and well-being disconfirms hypothesis 4. These findings suggest that spending time playing online games with friends did not significantly affect the relationship between online/offline support and well-being. There are a number of explanations for this finding. It could be that not enough participants were gamers and a larger sample is required to find these effects. It might also be expected that, since online social 27

33 support comes in so many forms besides online gaming, participant's rating of online social support was likely more based on calling, texting, and social networking. If the sample contained more active online gamers, it's possible that an effect would have been found. More research with larger samples comparing gamers to non-gamers are needed to pursue these hypotheses. More investigation is also needed into different types of games and how that affects levels of support and subsequent well-being. For example, MMOs provide different outlets for social interaction than First Person Shooters (FPS) do, though both can be played online with friends. One limitation was that the sample was composed entirely of college students. It is not clear that these results can be extrapolated to other age groups and demographics. Other things could have been measured that would affect online social support, such as type of online game played, as different games have different social features. In addition, social skills could be a factor in predicting amount of support offline and online. Implications Both offline and online versions of the MSPSS were found to have good reliability and validity, as well as similar factor structures. This indicates that both versions are well validated measures. Findings indicate that online social support is a good predictor of psychological well-being. This means that phones and the internet are good tools to use to communicate with other. We should recommend these tools to increase the well-being of others. Offline and online support were found to have similar correlations with well-being, suggesting that online support is just as valuable as offline support. More studies need to compare the impact of online vs. offline support on well-being before conclusions can be certain. More research is needed on the differences between those that play online games with friends and those that don't before conclusions can be made. 28

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